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As a learning exercise/practice I created a very simple SVM model using the ksvm function under the kernlab package.

The predictors I used were: VIX, Crude, Gold and Previous Day’s Close (factor).

The results were factors either returning 1 or 0 if the following day’s close was higher than the previous.

The train/test data set start date is 2007-12-31. I first tried data partitioning (75/25) the train and test sets in a random fashion irrespective of time-series. Then I tried testing it in slices (75/25) so time was continuous. Results were very little changed.

Here is how the SVM model performed using those 4 variables:

The results were fairing unimpressive but better than 50/50 is a great start.